02 Linear regression with multiple independent variables
03 Analysis of covariance
04 Multi-factor analysis of variance
05 Dimension reduction
06 Logistic regression
07 Diagnostic tests
Topics to be covered, 2 of 2
What you will learn
08 Survival analysis
09 Meta-analysis
10 Dark side of data science
11 Hierarchical models
12 Longitudinal data
13 Bayesian statistics
Module 01, Review
Simple linear regression
One factor analysis of variance
Module 01, SPSS scatterplot
Module 01, SPSS boxplot
Module 01, SPSS calculation of R Square
Module 01, SPSS ANOVA table
Module 01, SPSS linear regression coefficients
Break #1
What you have learned
01 Linear regresion, analysis of variance
What’s coming next
02 Linear regression with multiple independent variables
Module 02, Linear regression with multiple independent variables
Analysis of variance table
R-squared
Partial F tests
Stepwise regression
Interpretation
Collinearity
Mediation
Module 02, Checking assumptions
Non-normality
Q-Q plot of residuals
Lack of independence
Assessed qualitatively
Unequal variances, Non-linearity
Residual scatterplot
Module 02, SPSS dialog box for the general linear model
Module 02, SPSS computation of R-squared
10,548.480/15,079.017 = 0.70
Module 02, SPSS computation of change in R-squared
\(Partial\ R^2=0.700-0.693=0.007\)
Module 02, SPSS computation of partial F-test
Module 02, SPSS computation of full regression model
Module 02, SPSS computation of collinearity statistics
Module 2, What is mediation?
“A situation when the relationship between a predictor variable and an outcome variable can be explained by their relationship to a third variable (the mediator)”
Andy Field, Section 11.4
Module 2, SPSS assessment of mediation
Module 02, SPSS Q-Q plot
Module 02, SPSS Scatterplot, 1 of 4
Module 02, SPSS Scatterplot, 2 of 4
Module 02, SPSS Scatterplot, 3 of 4
Module 02, SPSS Scatterplot, 4 of 4
Break #2
What you have learned
02 Linear regression with multiple independent variables
What’s coming next
03 Analysis of covariance
Module 03, Analysis of covariance
Confounding/covariate imbalance
Interpretation
Interactions
Module 03, Checking assumptions
Non-normality
Q-Q plot of residuals
Lack of independence
Assessed qualitatively
Unequal variances, Non-linearity
Residual scatterplots
Module 03, SPSS calculation of unadjusted estimates
Module 03, SPSS calculation of adjusted estimates
Module 03, SPSS visualization, 1 of 2
Module 03, SPSS visualization, 2 of 2
Module 03, SPSS Q-Q plot
Module 03, SPSS scatterplot
Module 03, SPSS interaction test
Break #3
What you have learned
03 Analysis of covariance
What’s coming next
04 Multi-factor analysis of variance
Module 04, Multi-factor analysis of variance
Tukey post hoc test
Interaction
Module 04, Checking assumptions
Non-normality
Q-Q plot of residuals
Lack of independence
Assessed qualitatively
Unequal variances
Boxplots
Module 04, SPSS crosstabulation
Module 04, SPSS analysis of variance table
Module 04, SPSS removing irrelevant rows
Module 04, SPSS parameter estimates
Module 04, SPSS Tukey test
Module 04, SPSS Q-Q plot
Module 04, SPSS scatterplot
Module 04, SPSS, Box plots of exercise data
Module 04, SPSS, Mean values for the interaction
Module 04, SPSS, Analysis of variance table for interaction model
Module 04, SPSS, Parameter estimates for the interaction model
Module 04, SPSS, Interaction plot, 1 of 2
Module 04, SPSS, Interaction plot, 2 of 2
Module 04, When you can’t estimate an interaction
Special case, n=1
Only one observation for categorical combination
Module 04, SPSS, Example, full moon study, 1 of 2
Module 04, SPSS, Example, full moon study, 2 of 2
Module 04, SPSS, Interaction between exercise program and hours spent exercising
Module 04, SPSS, Testing for interaction in analysis of covariance
Module 04, SPSS, Table with irrelevant rows removed